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Bayesian Estimation of Dynamic Discrete Choice Models

  • Susumu Imai

    ()

    (Queen's University)

  • Neelam Jain

    ()

    (Northern Illinois University)

  • Andrew Ching

    ()

    (University of Toronto)

We propose a new methodology for structural estimation of dynamic discrete choice models. We combine the Dynamic Programming (DP) solution algorithm with the Bayesian Markov Chain Monte Carlo algorithm into a single algorithm that solves the DP problem and estimates the parameters simultaneously. As a result, the computational burden of estimating a dynamic model becomes comparable to that of a static model. Another feature of our algorithm is that even though per solution-estimation iteration, the number of grid points on the state variable is small, the number of effective grid points increases with the number of estimation iterations. This is how we help ease the "Curse of Dimensionality". We simulate and estimate several versions of a simple model of entry and exit to illustrate our methodology. We also prove that under standard conditions, the parameters converge in probability to the true posterior distribution, regardless of the starting values.

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File URL: http://qed.econ.queensu.ca/working_papers/papers/qed_wp_1118.pdf
File Function: First version 2006
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Paper provided by Queen's University, Department of Economics in its series Working Papers with number 1118.

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Length: 77 pages
Date of creation: Dec 2006
Date of revision:
Handle: RePEc:qed:wpaper:1118
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  1. Hardle, Wolfgang & Linton, Oliver, 1986. "Applied nonparametric methods," Handbook of Econometrics, in: R. F. Engle & D. McFadden (ed.), Handbook of Econometrics, edition 1, volume 4, chapter 38, pages 2295-2339 Elsevier.
  2. repec:cup:cbooks:9780521429504 is not listed on IDEAS
  3. Tülin Erdem & Michael P. Keane, 1996. "Decision-Making Under Uncertainty: Capturing Dynamic Brand Choice Processes in Turbulent Consumer Goods Markets," Marketing Science, INFORMS, vol. 15(1), pages 1-20.
  4. Victor Aguirregabiria & Pedro Mira, 2002. "Swapping the Nested Fixed Point Algorithm: A Class of Estimators for Discrete Markov Decision Models," Econometrica, Econometric Society, vol. 70(4), pages 1519-1543, July.
  5. Houser, Daniel, 2003. "Bayesian analysis of a dynamic stochastic model of labor supply and saving," Journal of Econometrics, Elsevier, vol. 113(2), pages 289-335, April.
  6. Imai, Susumu & Krishna, Kala, 2001. "Employment, Dynamic Deterrence and Crime," Working Papers 1-01-2, Pennsylvania State University, Department of Economics.
  7. repec:cup:etheor:v:12:y:1996:i:3:p:409-31 is not listed on IDEAS
  8. Siddhartha Chib & Edward Greenberg, 1994. "Markov Chain Monte Carlo Simulation Methods in Econometrics," Econometrics 9408001, EconWPA, revised 24 Oct 1994.
  9. Susumu Imai & Michael P. Keane, 2004. "Intertemporal Labor Supply and Human Capital Accumulation," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 45(2), pages 601-641, 05.
  10. Geweke, John & Houser, Dan & Keane, Michael, 1999. "Simulation Based Inference for Dynamic Multinomial Choice Models," MPRA Paper 54279, University Library of Munich, Germany.
  11. Andriy Norets, 2009. "Inference in Dynamic Discrete Choice Models With Serially orrelated Unobserved State Variables," Econometrica, Econometric Society, vol. 77(5), pages 1665-1682, 09.
  12. Lancaster, Tony, 1997. "Exact Structural Inference in Optimal Job-Search Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(2), pages 165-79, April.
  13. Hotz, V Joseph & Robert A. Miller & Seth Sanders & Jeffrey Smith, 1994. "A Simulation Estimator for Dynamic Models of Discrete Choice," Review of Economic Studies, Wiley Blackwell, vol. 61(2), pages 265-89, April.
  14. McCulloch, Robert & Rossi, Peter E., 1994. "An exact likelihood analysis of the multinomial probit model," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 207-240.
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